According to a 2008 OECD review of national policies for education in South Africa, typically only 15 percent to 18 percent of secondary school students who sit for their final year exams every year qualify automatically for university-level education; and this number seems to be decreasing as more students choose to complete subjects on so-called standard grade instead of higher grade, a trend that is especially apparent for mathematics and science, the two fields with critical skills shortages in the country. The South African tertiary education sector is quite small for a country with a population of around 50 million, with 11 "traditional" universities, 6 technical universities, and 6 comprehensive universities. The latter university types focus on more technical or vocational education. The public sector also funds 16 research institutions. In spite of these obstacles, South African universities participate in world-class research activities in many fields and range among the best on the African continent.

The reports should address the following questions: 1. When and why did the competition start? How many times has the competition been held since its inception? How frequently is the competition held and is it colocated with other events?

Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algorithms. In order to limit the search space and escape from local maxima produced by executing EM algorithm, this paper presents a learning parameter algorithm that is a fusion of EM and RBE algorithms. This algorithm incorporates the range of a parameter into the EM algorithm. This range is calculated by the first step of RBE algorithm allowing a regularization of each parameter in bayesian network after the maximization step of the EM algorithm. The threshold EM algorithm is applied in brain tumor diagnosis and show some advantages and disadvantages over the EM algorithm.

In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorithm and Bayesian network for image classification. First, we use the technique of tangent distance to calculate several tangent spaces representing the same image. The objective is to reduce the error in the classification phase. Second, we cut the image in a whole of blocks. For each block, we compute a vector of descriptors. Then, we use K-means to cluster the low-level features including color and texture information to build a vector of labels for each image. Finally, we apply five variants of Bayesian networks classifiers (Na\"ive Bayes, Global Tree Augmented Na\"ive Bayes (GTAN), Global Forest Augmented Na\"ive Bayes (GFAN), Tree Augmented Na\"ive Bayes for each class (TAN), and Forest Augmented Na\"ive Bayes for each class (FAN) to classify the image of faces using the vector of labels. In order to validate the feasibility and effectively, we compare the results of GFAN to FAN and to the others classifiers (NB, GTAN, TAN). The results demonstrate FAN outperforms than GFAN, NB, GTAN and TAN in the overall classification accuracy.

The majority of events encountered in everyday life are not well described based on their occurrence at a particular point in time but rather they are described by a set of observations that can produce a comprehensive final event. Thus, time is an important dimension to take into account in reasoning and in the field of artificial intelligence in general. To add the time dimension in Bayesian networks, different approaches have been proposed. The common names used to describe this new dimension are "temporal" and "dynamic ". A. Definition II.

Large bilingual parallel texts (also known as bitexts) are usually stored in a compressed form, and previous work has shown that they can be more efficiently compressed if the fact that the two texts are mutual translations is exploited. For example, a bitext can be seen as a sequence of biwords ---pairs of parallel words with a high probability of co-occurrence--- that can be used as an intermediate representation in the compression process. However, the simple biword approach described in the literature can only exploit one-to-one word alignments and cannot tackle the reordering of words. We therefore introduce a generalization of biwords which can describe multi-word expressions and reorderings. We also describe some methods for the binary compression of generalized biword sequences, and compare their performance when different schemes are applied to the extraction of the biword sequence. In addition, we show that this generalization of biwords allows for the implementation of an efficient algorithm to look on the compressed bitext for words or text segments in one of the texts and retrieve their counterpart translations in the other text ---an application usually referred to as translation spotting--- with only some minor modifications in the compression algorithm.

The first indication of a new outbreak is often in unstructured data (natural language) and reported openly in traditional or social media as a new `flu-like' or `malaria-like' illness weeks or months before the new pathogen is eventually isolated. We present a system for tracking these early signals globally, using natural language processing and crowdsourcing. By comparison, search-log-based approaches, while innovative and inexpensive, are often a trailing signal that follow open reports in plain language. Concentrating on discovering outbreak-related reports in big open data, we show how crowdsourced workers can create near-real-time training data for adaptive active-learning models, addressing the lack of broad coverage training data for tracking epidemics. This is well-suited to an outbreak information-flow context, where sudden bursts of information about new diseases/locations need to be manually processed quickly at short notice.

Service-Oriented Computing (SOC) has received much interest due to its potential to tackle many adaptive system architecture issues that were previously hard to overcome by other computing paradigms. However, it has been facing great difficulty in quickly discovering and dynamically combing available Web services to satisfy given request on-demand. Most of the current researches concentrated o n the semantic model for service discovery, composition, and so on. But there are few studies concerned the intrinsic pattern and law of the service interactions and relationships. To achiev e the vision of SOC in heterogeneous and open environment, in our opinion, not only the semantics of individual Web service but also the interactions and relationships among Web services are needed to be considered seriously. In this paper, beginning with combining Semantic Web and social networking technology within SOC paradigm, we study associations between Web services, mine the relationships among services to design and build Service Network (SN), anal y z e the structural and social characteristics and complexity of SN to reveal the user interests, business requests, information and data flow and direction. In short, we would like to reassess and reconsider the SOC paradigm from the network perspective, through finding new knowledge to build new theoretical basis and approach which can be used to guide and promote the service discovery, composition, and so on, in SOC paradigm.

Illegal extraction of forest resources is fought, in many developing countries, by patrols through the forest that seek to deter such activity by decreasing its profitability. With limited resources for performing such patrols, a patrol strategy will seek to distribute the patrols throughout the forest, in space and time, in order to minimize the resulting amount of extraction that occurs or maximize the degree of forest protection, according to one of several potential metrics. We pose this problem as a Stackelberg game. We adopt and extend the simple, geometrically elegant model of (Albers 2010). First, we study optimal allocations of patrol density under generalizations of this model, relaxing several of its assumptions. Second, we pose the problem of generating actual schedules whose site visit frequencies are consistent with the analytically computed optimal patrol densities.

A number of real-world security scenarios can be cast as a problem of transiting an area guarded by a mobile patroller, where the transiting agent aims to choose its route so as to minimize the probability of encountering the patrolling agent, and vice versa. We model this problem as a two-player zero-sum game on a graph, termed the transit game. In contrast to the existing models of area transit, where one of the players is stationary, we assume both players are mobile. We also explicitly model the limited endurance of the patroller and the notion of a base to which the patroller has to repeatedly return. Noting the prohibitive size of the strategy spaces of both players, we develop single- and double-oracle based algorithms including a novel acceleration scheme, to obtain optimum route selection strategies for both players. We evaluate the developed approach on a range of transit game instances inspired by real-world security problems in the urban and naval security domains.